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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Professional 免費考試真題:

1. A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A) Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
B) The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
C) Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.
D) The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
E) Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.

2. Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

A) /jobs/get
B) /jobs/list
C) /jobs/runs/list
D) /jobs/runs/get
E) /jobs/runs/get-output

3. A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A) Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
B) Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.
C) Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
D) All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
E) Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.

4. A production cluster has 3 executor nodes and uses the same virtual machine type for the driver and executor.
When evaluating the Ganglia Metrics for this cluster, which indicator would signal a bottleneck caused by code executing on the driver?

A) Network I/O never spikes
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B) Bytes Received never exceeds 80 million bytes per second
C) Overall cluster CPU utilization is around 25%
D) Total Disk Space remains constant
E) The five Minute Load Average remains consistent/flat

5. Which statement describes integration testing?

A) Requires an automated testing framework
B) Validates an application use case
C) Validates interactions between subsystems of your application
D) Validates behavior of individual elements of your application
E) Requires manual intervention

問題與答案：